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✨ Python Inner Functions: What Are They Good For? ✨

📖 Learn how to create inner functions in Python to access nonlocal names, build stateful closures, and create decorators.

🏷️ #intermediate #python

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1.27K views16:33

https://news.1rj.ru/str/addlist/8_rRW2scgfRhOTc0

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅

✅

https://news.1rj.ru/str/Codeprogrammer

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✨ abstract method | Python Glossary ✨

📖 A method that is marked with @abstractmethod.

🏷️ #Python

1.23K views12:02

✨ dependency | Python Glossary ✨

📖 An external package that your project needs in order to run, build, or be developed.

🏷️ #Python

1.31K views12:17

✨ Git | Python Tools ✨

📖 A distributed version control system.

🏷️ #Python

1.1K views12:02

An incredibly short book, but with a deep analysis of the internal mechanisms of Python, which we use every day. ❤️

Each chapter contains an explanation of a specific language feature, such as working with *args/**kwargs, mutable arguments, generators, decorators, context managers, enumerate/zip, exceptions, dunder methods, and other clever constructs.

Link: https://book.pythontips.com/en/latest/

👉

@DataScience4

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1.01K viewsedited 06:21

✨ flake8 | Python Tools ✨

📖 A command-line Python linter.

🏷️ #Python

954 views12:02

Forwarded from Machine Learning with Python

Real Python.pdf

332 KB

Real Python - Pocket Reference (Important)

#python #py #PythonTips #programming

https://news.1rj.ru/str/CodeProgrammer

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707 views14:29

✨ pytest | Python Tools ✨

📖 A test runner and framework for Python.

🏷️ #Python

638 views12:02

✨ Pylint | Python Tools ✨

📖 A static code checker for Python.

🏷️ #Python

669 views12:17

✨ Quiz: Writing DataFrame-Agnostic Python Code With Narwhals ✨

📖 If you're a Python library developer wondering how to write DataFrame-agnostic code, the Narwhals library is the solution you're looking for.

🏷️ #advanced #data-science #python

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691 views12:32

Tip: Efficiently Slice Iterators and Large Sequences with itertools.islice

Explanation:
Traditional list slicing (my_list[start:end]) creates a new list in memory containing the sliced elements. While convenient for small lists, this becomes memory-inefficient for very large lists and is impossible for pure iterators (like generators or file objects) that don't support direct indexing.

itertools.islice provides a memory-optimized solution by returning an iterator that yields elements from a source iterable (list, generator, file, etc.) between specified start, stop (exclusive), and step indices, without first materializing the entire slice into a new collection. This "lazy" consumption of the source iterable is crucial for processing massive datasets, infinite sequences, or streams where only a portion is needed, preventing excessive memory usage and improving performance. It behaves syntactically similar to standard slicing but operates at the iterator level.

Example:

import itertools
import sys

# A generator for a very large sequence
def generate_large_sequence(count):
    for i in range(count):
        yield f"Data_Item_{i}"

# Imagine needing to process only a small segment of 10 million items
total_items = 10**7
data_stream = generate_large_sequence(total_items)

# Get items from index 500 to 509 (inclusive)
# Using islice:
print("--- Using itertools.islice ---")
# islice(iterable, [start], stop, [step])
# Here, start=500, stop=510 (exclusive)
for item in itertools.islice(data_stream, 500, 510):
    print(item)

# Compare memory usage (conceptual, as actual list materialization would be massive)
# If you tried:
# large_list = list(generate_large_sequence(total_items)) # <-- HUGE memory consumption here!
# for item in large_list[500:510]:
#    print(item)

# islice consumes minimal memory, only holding iterator state.
# The `data_stream` generator itself only holds its current state, not the whole sequence.
print("\n`itertools.islice` memory footprint is negligible compared to creating a full list slice.")

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By: @DataScience4 ✨

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382 viewsedited 10:58

I just discovered schemdraw — a Python library that turns code into neat and clear electrical schematics.

No more dragging wires around in inconvenient GUIs.

Clean code for resistors, logic elements, and much more.

Full customization of all elements.

pip install schemdraw and you can start drawing.

👉

@DataScience4

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387 viewsedited 11:55

✨ mypy | Python Tools ✨

📖 A static type checker for Python.

🏷️ #Python

392 views12:02

✨ isort | Python Tools ✨

📖 A command-line utility and library for sorting and organizing Python imports.

🏷️ #Python

378 views12:17

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Forwarded from Machine Learning with Python

I rarely say this, but this is the best repository for mastering Python.

The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley).

In this PythonMastery.pdf, all the information is structured
👾 Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf

In the Exercises folder, all the exercises are located
👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises

In the Solutions folder — the solutions
👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions

👉

@codeprogrammer

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